Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Haptic-Assisted Collaborative Robot Framework for Improved Situational Awareness in Skull Base Surgery (2401.11709v1)

Published 22 Jan 2024 in cs.RO, cs.SY, and eess.SY

Abstract: Skull base surgery is a demanding field in which surgeons operate in and around the skull while avoiding critical anatomical structures including nerves and vasculature. While image-guided surgical navigation is the prevailing standard, limitation still exists requiring personalized planning and recognizing the irreplaceable role of a skilled surgeon. This paper presents a collaboratively controlled robotic system tailored for assisted drilling in skull base surgery. Our central hypothesis posits that this collaborative system, enriched with haptic assistive modes to enforce virtual fixtures, holds the potential to significantly enhance surgical safety, streamline efficiency, and alleviate the physical demands on the surgeon. The paper describes the intricate system development work required to enable these virtual fixtures through haptic assistive modes. To validate our system's performance and effectiveness, we conducted initial feasibility experiments involving a medical student and two experienced surgeons. The experiment focused on drilling around critical structures following cortical mastoidectomy, utilizing dental stone phantom and cadaveric models. Our experimental results demonstrate that our proposed haptic feedback mechanism enhances the safety of drilling around critical structures compared to systems lacking haptic assistance. With the aid of our system, surgeons were able to safely skeletonize the critical structures without breaching any critical structure even under obstructed view of the surgical site.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (26)
  1. A. T. Meybodi, G. Mignucci-Jiménez, M. T. Lawton, J. K. Liu, M. C. Preul, and H. Sun, “Comprehensive microsurgical anatomy of the middle cranial fossa: Part I—Osseous and meningeal anatomy,” Frontiers in Surgery, vol. 10, 2023.
  2. B. J. Gantz, “Evolution of otology and neurotology education in the United States,” Otology & Neurotology, vol. 39, no. 4S, pp. S64–S68, 2018.
  3. N. Zagzoog and V. X. Yang, “State of robotic mastoidectomy: literature review,” World Neurosurgery, vol. 116, pp. 347–351, 2018.
  4. M. Bennett, F. Warren, and D. Haynes, “Indications and technique in mastoidectomy,” Otolaryngologic Clinics of North America, vol. 39, no. 6, pp. 1095–1113, 2006.
  5. A. Danilchenko, R. Balachandran, J. L. Toennies, S. Baron, B. Munske, J. M. Fitzpatrick, T. J. Withrow, R. J. Webster III, and R. F. Labadie, “Robotic mastoidectomy,” Otology & neurotology: official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology, vol. 32, no. 1, p. 11, 2011.
  6. H. Lim, N. Matsumoto, B. Cho, J. Hong, M. Yamashita, M. Hashizume, and B.-J. Yi, “Semi-manual mastoidectomy assisted by human–robot collaborative control–a temporal bone replica study,” Auris Nasus Larynx, vol. 43, no. 2, pp. 161–165, 2016.
  7. S. A. Bowyer, B. L. Davies, and F. R. y Baena, “Active constraints/virtual fixtures: A survey,” IEEE Transactions on Robotics, vol. 30, no. 1, pp. 138–157, 2013.
  8. N. Simaan, R. H. Taylor, and H. Choset, “Intelligent surgical robots with situational awareness,” Mechanical Engineering, vol. 137, no. 09, pp. S3–S6, 2015.
  9. A. Attanasio, B. Scaglioni, E. De Momi, P. Fiorini, and P. Valdastri, “Autonomy in surgical robotics,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 4, pp. 651–679, 2021.
  10. N. P. Dillon, L. Fichera, P. S. Wellborn, R. F. Labadie, and R. J. Webster, “Making robots mill bone more like human surgeons: using bone density and anatomic information to mill safely and efficiently,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2016, pp. 1837–1843.
  11. A. S. Ding, S. Capostagno, C. R. Razavi, Z. Li, R. H. Taylor, J. P. Carey, and F. X. Creighton, “Volumetric accuracy analysis of virtual safety barriers for cooperative-control robotic mastoidectomy,” Otology & neurotology: official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology, vol. 42, no. 10, p. e1513, 2021.
  12. T. Xia, C. Baird, G. Jallo, K. Hayes, N. Nakajima, N. Hata, and P. Kazanzides, “An integrated system for planning, navigation and robotic assistance for skull base surgery,” Intl J Medical Robotics and Computer Assisted Surgery, vol. 4, no. 4, pp. 321–330, Dec 2008.
  13. M. H. Yoo, H. S. Lee, C. J. Yang, S. H. Lee, H. Lim, S. Lee, B.-J. Yi, and J. W. Chung, “A cadaver study of mastoidectomy using an image-guided human–robot collaborative control system,” Laryngoscope investigative otolaryngology, vol. 2, no. 5, pp. 208–214, 2017.
  14. J. J. Abbott, P. Marayong, and A. M. Okamura, “Haptic virtual fixtures for robot-assisted manipulation,” in Robotics Research: Results of the 12th International Symposium ISRR.   Springer, 2007, pp. 49–64.
  15. H. Ishida, J. A. Barragan, A. Munawar, Z. Li, P. Kazanzides, M. Kazhdan, D. Trakimas, F. X. Creighton, and R. H. Taylor, “Improving surgical situational awareness with signed distance field: a pilot study in virtual reality,” arXiv preprint arXiv:2303.01733, 2023.
  16. A. Munawar, Y. Wang, R. Gondokaryono, and G. S. Fischer, “A real-time dynamic simulator and an associated front-end representation format for simulating complex robots and environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov 2019, pp. 1875–1882.
  17. A. Fedorov, R. Beichel, J. Kalpathy-Cramer, J. Finet, J.-C. Fillion-Robin, S. Pujol, C. Bauer, D. Jennings, F. Fennessy, M. Sonka, J. Buatti, S. Aylward, J. V. Miller, S. Pieper, and R. Kikinis, “3D Slicer as an image computing platform for the quantitative imaging network,” Magnetic Resonance Imaging, vol. 30, no. 9, pp. 1323–1341, 2012, quantitative Imaging in Cancer. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0730725X12001816
  18. Y.-H. Su, A. Munawar, A. Deguet, A. Lewis, K. Lindgren, Y. Li, R. H. Taylor, G. S. Fischer, B. Hannaford, and P. Kazanzides, “Collaborative Robotics Toolkit (CRTK): Open software framework for surgical robotics research,” in 2020 Fourth IEEE International Conference on Robotic Computing (IRC), 2020, pp. 48–55.
  19. T. Saito and J.-I. Toriwaki, “New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications,” Pattern Recognition, vol. 27, no. 11, pp. 1551–1565, Nov. 1994.
  20. A. Munawar, Z. Li, N. Nagururu, D. Trakimas, P. Kazanzides, R. H. Taylor, and F. X. Creighton, “Fully immersive virtual reality for skull-base surgery: Surgical training and beyond,” International Journal of Computer Assisted Radiology and Surgery (IJCARS), 2023.
  21. H. Shu, R. Liang, Z. Li, A. Goodridge, X. Zhang, H. Ding, N. Nagururu, M. Sahu, F. X. Creighton, R. H. Taylor, et al., “Twin-s: a digital twin for skull base surgery,” International Journal of Computer Assisted Radiology and Surgery, pp. 1–8, 2023.
  22. K. C. Olds, P. Chalasani, P. Pacheco-Lopez, I. Iordachita, L. M. Akst, and R. H. Taylor, “Preliminary evaluation of a new microsurgical robotic system for head and neck surgery,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2014, pp. 1276–1281.
  23. K. Olds, “Robotic assistant systems for otolaryngology-head and neck surgery,” Ph.D. dissertation, Johns Hopkins University, 2015.
  24. Y. Chen, A. Goodridge, M. Sahu, A. Kishore, S. Vafaee, H. Mohan, K. Sapozhnikov, F. X. Creighton, R. H. Taylor, and D. Galaiya, “A force-sensing surgical drill for real-time force feedback in robotic mastoidectomy,” International Journal of Computer Assisted Radiology and Surgery, pp. 1–8, 2023.
  25. A. S. Ding, A. Lu, Z. Li, M. Sahu, D. Galaiya, J. H. Siewerdsen, M. Unberath, R. H. Taylor, and F. X. Creighton, “A self-configuring deep learning network for segmentation of temporal bone anatomy in cone-beam CT imaging,” Otolaryngology–Head and Neck Surgery, 2023.
  26. H. Zhang, L. Zhu, J. Shen, and A. Song, “Implicit neural field guidance for teleoperated robot-assisted surgery,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 6866–6872.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (8)
  1. Hisashi Ishida (8 papers)
  2. Manish Sahu (12 papers)
  3. Adnan Munawar (16 papers)
  4. Nimesh Nagururu (5 papers)
  5. Deepa Galaiya (3 papers)
  6. Peter Kazanzides (35 papers)
  7. Francis X. Creighton (11 papers)
  8. Russell H. Taylor (55 papers)
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now